911 Emergency

Analyzing and Visualizing 911 Calls by GoogleVis

This kernel serves the purpose of presenting quick data analytic with Google Visulization. The analysis will mainly focus on two aspects: Geographical information and time stamp of the calls.

Fire up

library(googleVis)
library(sqldf)
library(ggmap)
library(ggplot2)
Sys.setenv(LANG = "en")
Sys.setlocale("LC_TIME", "C")

## [1] "C"

data<-read.csv('911.csv')
df<-data[,c('lat','lng','title','timeStamp')]
summary(df)

##       lat             lng                                 title      
##  Min.   :30.33   Min.   :-95.60   Traffic: VEHICLE ACCIDENT -:27981  
##  1st Qu.:40.10   1st Qu.:-75.39   Traffic: DISABLED VEHICLE -: 9240  
##  Median :40.14   Median :-75.30   Fire: FIRE ALARM           : 6790  
##  Mean   :40.16   Mean   :-75.32   EMS: RESPIRATORY EMERGENCY : 6077  
##  3rd Qu.:40.23   3rd Qu.:-75.21   EMS: CARDIAC EMERGENCY     : 6025  
##  Max.   :41.17   Max.   :-75.00   EMS: FALL VICTIM           : 5904  
##                                   (Other)                    :58501  
##                timeStamp     
##  2015-12-10 17:40:01:     8  
##  2015-12-23 14:12:01:     7  
##  2016-04-21 17:57:01:     7  
##  2015-12-23 16:07:01:     6  
##  2015-12-29 10:12:01:     6  
##  2016-02-02 07:22:02:     6  
##  (Other)            :120478

Data Pre-processing

Before analysis, we first perform data preprocessing step for a more neat dataset. First, we split the title of the event.

Next, we will deal with timeStamp of the 911 calls.

df<-data.frame(df,Date=NA,Time=NA,Hour=NA,Days=NA,Period=NA)
df$Date<-sapply(df$timeStamp,function(x) strsplit(as.character(x),' ')[[1]][1])
df$Time <- sapply(df$timeStamp,function(x) strsplit(as.character(x),' ')[[1]][2] )
df$Hour<-sapply(df$Time,function(x) as.numeric(strsplit(as.character(x),':')[[1]][1]))
df$Period<-sapply(df$Hour,function(x){
  nu<-x
  if(nu>6&&nu<=12){
   'Morining'
  }else if(nu>12&&nu<=18){
    'Afternoon'
  }else if(nu>18&&nu<=24){
     'Evening'
  }else{
    'Midnight'
    }
}
)
c<-as.Date(df$Date)
df$Date<-c
cc<-weekdays(c)
df$Days<-cc

Then, the data are all in the form we want. I will divide the analytics in terms of the type of events so the whole procedure will be transparent and efficient.

Build functions for analytic

In order to make the analysis and plot more direct and efficient, I choose to wrap up the analyzes into differnt functions so that the subsquent procedures will be boosted.

A: Where do these calls happen?

map_d<-qmap('Philadelphia', zoom = 9, maptype = 'hybrid')
Map<-function(item){
  color<-list('EMS'='Yellow','Fire'='red','Traffic'='blue')
  if(item=='All'){
    tmp<-df
  }else{
    tmp<-df[df$Event_type==item,]
  }
  if(item=='All'){
  plt<-map_d + geom_point(data = tmp, aes(x = lng, y = lat,color=Event_type), alpha=0.5)
  }
  else{
  plt<-map_d + geom_point(data = tmp, aes(x = lng, y = lat), color=color[item], alpha=0.5) 
  }
  return(plot(plt))
}

B: Calls among days of a week

days<-function(item){
if(item=='All'){
    tmp<-df
  }else{
    tmp<-df[df$Event_type==item,]
  }
tmp<-sqldf('SELECT Days,count(Days) as freq from tmp group by Days')
dayp<-gvisColumnChart(tmp,options = list(width=800,height=600))
return(dayp)
}

C: Calls among periods

period<-function(item){
if(item=='All'){
    tmp<-df
  }else{
    tmp<-df[df$Event_type==item,]
  }
tmp<-sqldf('SELECT Period,count(Period) as freq from tmp group by Period')
periodp<-gvisPieChart(tmp,options = list(width=600,height=400))
return(periodp)
}

D: Calls among date

DT<-function(item){
  if(item=='All'){
    tmp<-df
  }else{
    tmp<-df[df$Event_type==item,]
  }
tmp<-sqldf('SELECT Date,count(Date) as freq from tmp group by Date')
CalD <- gvisCalendar(tmp,datevar="Date", 
                    numvar="freq",
                    options=list(
                      title="Calls Records Around the Year",
                      width=800,
                      height=440,
                      calendar="{yearLabel: { fontName: 'Times-Roman',
                               fontSize: 32, color: '#1A8763', bold: true},
                               cellSize: 20,
                               cellColor: { stroke: 'red', strokeOpacity: 0.2 },
                               focusedCellColor: {stroke:'red'}}")
                    )
return(CalD)
}

E: Calls among Hours

HR<-function(item){
  if(item=='All'){
    tmp<-df
  }else{
    tmp<-df[df$Event_type==item,]
  }
tmp<-sqldf('SELECT Hour,count(Hour) as freq from tmp group by Hour')
HRP<-gvisColumnChart(tmp,options = list(width=800,height=400))
return(HRP)
}

F:The most frequent calling reasons

RE<-function(item){
tmp<-df[df$Event_type==item,] 
tmp<-sqldf('SELECT Event_detail,count(Event_detail) as freq from tmp group by Event_detail order by freq desc') 
Table <- gvisTable(tmp,options=list(page='enable',width=300,height=600))
return(Table)
}

Now use Google Visulization to see what we want

A: Explore All Types of Events

A: Where do they happen

Map('All')

B:Calls among the week

days('All')

Data: data • Chart ID: ColumnChartIDeb42bc45dbf • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

C:Calls among Day

period('All')

Data: tmp • Chart ID: PieChartIDeb443e03698 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

D: Date Records of the call

DT('All')

Data: tmp • Chart ID: CalendarIDeb4780d7670 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

E:Calls in hours

HR('All')

Data: data • Chart ID: ColumnChartIDeb449dc1370 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

EMS Visulization

A: Where do they happen

Map('EMS')

B:Calls among the week

days('EMS')

Data: data • Chart ID: ColumnChartIDeb44ba876bc • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

C:Calls among Day

period('EMS')

Data: tmp • Chart ID: PieChartIDeb4595f4a04 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

D: Date Records of the call

DT('EMS')

Data: tmp • Chart ID: CalendarIDeb45de320ef • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

E:Calls in hours

HR('EMS')

Data: data • Chart ID: ColumnChartIDeb4a1c1e0a • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

F: Details of Calls

RE('EMS')

Data: tmp • Chart ID: TableIDeb46e1e5328 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

Traffic Visulization

A: Where do they happen

Map('Traffic')

B:Calls among the week

days('Traffic')

Data: data • Chart ID: ColumnChartIDeb456966de7 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

C:Calls among Day

period('Traffic')

Data: tmp • Chart ID: PieChartIDeb417e676ca • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

D: Date Records of the call

DT('Traffic')

Data: tmp • Chart ID: CalendarIDeb471246b42 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

E:Calls in hours

HR('Traffic')

Data: data • Chart ID: ColumnChartIDeb447da20d1 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

F: Details of Calls

RE('Traffic')

Data: tmp • Chart ID: TableIDeb468b216ee • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

Fire Visulization

A: Where do they happen

Map('Fire')

B:Calls among the week

days('Fire')

Data: data • Chart ID: ColumnChartIDeb4229b51b3 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

C:Calls among Day

period('Fire')

Data: tmp • Chart ID: PieChartIDeb45bfe3f55 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

D: Date Records of the call

DT('Fire')

Data: tmp • Chart ID: CalendarIDeb4794bf49 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

E:Calls in hours

HR('Fire')

Data: data • Chart ID: ColumnChartIDeb465a81b0a • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy

F: Details of Calls

RE('Fire')

Data: tmp • Chart ID: TableIDeb45a634103 • googleVis-0.6.1
R version 3.3.1 (2016-06-21) • Google Terms of Use • Documentation and Data Policy